Estimating characteristic coefficient of vertical leaf nitrogen profile within wheat canopy from spectral reflectance
文献类型: 外文期刊
作者: Li, Heli 1 ; Yang, Guijun 1 ; Long, Huiling 2 ; Feng, Haikuan 2 ; Xu, Bo 2 ; Zhao, Chunjiang 1 ;
作者机构: 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, 11,Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China
关键词: Vertical leaf nitrogen profile; Characteristic coefficient; Spectral reflectance; Remote sensing; Robust model
期刊名称:COMPUTERS AND ELECTRONICS IN AGRICULTURE ( 影响因子:8.3; 五年影响因子:8.3 )
ISSN: 0168-1699
年卷期: 2023 年 206 卷
页码:
收录情况: SCI
摘要: The characteristic coefficient of vertical leaf nitrogen (N) profile is a canopy parameter that indicates the attenuation steepness of leaf N from the top of canopy downward. It is sensitive not only to crop production, grain yield and quality, and light-and N-use efficiency but also to N deficiency in the crop. We introduce this coefficient by exploring a robust method to estimate it from canopy spectral reflectance. We analyze compre-hensively various approaches based on diverse datasets of winter wheat. We test and compare the accuracy and stability of models by using the adjusted and weighted coefficient of determination (wRadj2), the mean absolute error (MAE), and the mean and coefficient of variation (CV) over multiple seasons. The analysis focuses mainly on the coefficient of mass-based leaf N profile (Km); nevertheless, a comparison with the coefficient of area-based leaf N profile is presented. The results indicate that the most robust model to estimate Km of winter wheat is Km = (1.8037RGVI -0.9702 + 0.0786 exp(0.6315/DASF))/2, where RGVI is the red and green ratio vegetation index, and DASF is the directional area scattering factor. The mean wRadj2 is 0.663 with CV = 8.2% and the mean MAE is 0.117 with CV = 12.8% over three seasons including various situations. This makes it possible to assess Km at large areas and follow its dynamics over multiple periods in a timely and nondestructive manner.
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